Automated Machine Learning (AutoML) is an area of research that focuses on developing methods to generate machine learning models automatically. The idea of being able to build machine learning models with very little human intervention represents a great opportunity for the practice of applied machine learning. However, there is very little information on how to design an AutoML system in practice. Most of the research focuses on the problems facing optimization algorithms and leaves out the details of how that would be done in practice. In this paper, we propose a frame of reference for building general AutoML systems. Through a narrative review of the main approaches in the area, our main idea is to distill the fundamental concepts in order to support them in a single design. Finally, we discuss some open problems related to the application of AutoML for future research.

翻译：自动化机器学习（AutoML）是一个致力于开发自动生成机器学习模型方法的研究领域。能够在极少人工干预下构建机器学习模型这一理念，为应用型机器学习的实践带来了巨大机遇。然而，关于如何在实践中设计AutoML系统的信息却极为匮乏。现有研究大多聚焦于优化算法面临的问题，而忽略了实际实现的具体细节。本文提出了一套用于构建通用AutoML系统的参考框架。通过对该领域主要方法进行叙述性综述，我们旨在提炼核心概念并将其整合到统一设计中。最后，我们讨论了与AutoML应用相关的一些开放性问题，为未来研究指明方向。